Aspect Extraction

Mukherjee and Liu (2012). Aspect Extraction through Semi-Supervised Modeling. ACL.

1. A key task of the framework is to extract aspects of entities that have been commented in opinion documents.
2. Two main types:
• The first type only extracts aspect terms without grouping them;
• The second type uses statistical topic models to extract aspects and group them.
3. This paper that given some seeds in the user interested categories.
4. The models are related to the DFLDA model in (Andrzejewski et al., 2009), while DF-LDA is only for topics/aspects.
5. There are many existing works on aspect extraction
• to find frequent noun terms and possibly with the help of dependency relations 
• to use supervised sequence labeling
6. Aspect and sentiment extraction using topic modeling come in two flavors:
• discovering aspect words sentiment wise (放在一起表示)
• separately discovering both aspects and sentiments (used Maximum-Entropy, Mei
  et al., 2007; Zhao et al., 2010)
• 思考上述兩種方法的優缺點，改進的空間
7. One problem with these existing models is that many discovered aspects are not understandable / meaningful to users.
8. Standard LDA and existing aspect and sentiment models based on document level, so many “non-specific” terms being pulled and clustered
9. Aspect terms tend to be nouns or noun phrases and sentiment terms tend to be adjectives, adverbs

Zhao et al., (2010). jointly modeling aspects and opinions with a mazEnt-LDA Hybrid. EMNLP.

1. Separateing aspects and opinion words can be very useful.
• can be used to construct a domain-dependent sentiment lexicon and applied to tasks such as sentiment classification. 
2. Global topic models may not be suitable for detecing rateable aspects.

Bagheri et al., (2013). An Unsupervised Aspect Detection Model for Sentiment Analysis of Reviews. NLDB.

1. Aspects are important because without knowing them, the opinions expressed in a sentence or a review are of limited use.